rendering/universal_tts/models/cosyvoice/cli/model.py

# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#               2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Generator
import torch
import numpy as np
import threading
import time
from torch.nn import functional as F
from contextlib import nullcontext
import uuid
from models.cosyvoice.utils.common import fade_in_out
from models.cosyvoice.utils.file_utils import (
    convert_onnx_to_trt,
    export_cosyvoice2_vllm,
)
from models.cosyvoice.utils.common import TrtContextWrapper


class CosyVoiceModel:

    def __init__(
        self,
        llm: torch.nn.Module,
        flow: torch.nn.Module,
        hift: torch.nn.Module,
        fp16: bool = False,
    ):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.llm = llm
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        self.token_min_hop_len = 2 * self.flow.input_frame_rate
        self.token_max_hop_len = 4 * self.flow.input_frame_rate
        self.token_overlap_len = 20
        # mel fade in out
        self.mel_overlap_len = int(
            self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256
        )
        self.mel_window = np.hamming(2 * self.mel_overlap_len)
        # hift cache
        self.mel_cache_len = 20
        self.source_cache_len = int(self.mel_cache_len * 256)
        # speech fade in out
        self.speech_window = np.hamming(2 * self.source_cache_len)
        # rtf and decoding related
        self.stream_scale_factor = 1
        assert (
            self.stream_scale_factor >= 1
        ), "stream_scale_factor should be greater than 1, change it according to your actual rtf"
        self.llm_context = (
            torch.cuda.stream(torch.cuda.Stream(self.device))
            if torch.cuda.is_available()
            else nullcontext()
        )
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.mel_overlap_dict = {}
        self.flow_cache_dict = {}
        self.hift_cache_dict = {}
        self.silent_tokens = []

    def load(self, llm_model, flow_model, hift_model):
        self.llm.load_state_dict(
            torch.load(llm_model, map_location=self.device, weights_only=True),
            strict=True,
        )
        self.llm.to(self.device).eval()
        self.flow.load_state_dict(
            torch.load(flow_model, map_location=self.device, weights_only=True),
            strict=True,
        )
        self.flow.to(self.device).eval()
        # in case hift_model is a hifigan model
        hift_state_dict = {
            k.replace("generator.", ""): v
            for k, v in torch.load(
                hift_model, map_location=self.device, weights_only=True
            ).items()
        }
        self.hift.load_state_dict(hift_state_dict, strict=True)
        self.hift.to(self.device).eval()

    def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model):
        llm_text_encoder = torch.jit.load(
            llm_text_encoder_model, map_location=self.device
        )
        self.llm.text_encoder = llm_text_encoder
        llm_llm = torch.jit.load(llm_llm_model, map_location=self.device)
        self.llm.llm = llm_llm
        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
        self.flow.encoder = flow_encoder

    def load_trt(
        self,
        flow_decoder_estimator_model,
        flow_decoder_onnx_model,
        trt_concurrent,
        fp16,
    ):
        assert torch.cuda.is_available(), "tensorrt only supports gpu!"
        if (
            not os.path.exists(flow_decoder_estimator_model)
            or os.path.getsize(flow_decoder_estimator_model) == 0
        ):
            convert_onnx_to_trt(
                flow_decoder_estimator_model,
                self.get_trt_kwargs(),
                flow_decoder_onnx_model,
                fp16,
            )
        del self.flow.decoder.estimator
        import tensorrt as trt

        with open(flow_decoder_estimator_model, "rb") as f:
            estimator_engine = trt.Runtime(
                trt.Logger(trt.Logger.INFO)
            ).deserialize_cuda_engine(f.read())
        assert estimator_engine is not None, "failed to load trt {}".format(
            flow_decoder_estimator_model
        )
        self.flow.decoder.estimator = TrtContextWrapper(
            estimator_engine, trt_concurrent=trt_concurrent, device=self.device
        )

    def get_trt_kwargs(self):
        min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)]
        opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)]
        max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)]
        input_names = ["x", "mask", "mu", "cond"]
        return {
            "min_shape": min_shape,
            "opt_shape": opt_shape,
            "max_shape": max_shape,
            "input_names": input_names,
        }

    def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
        cur_silent_token_num, max_silent_token_num = 0, 5
        with self.llm_context, torch.cuda.amp.autocast(
            self.fp16 is True and hasattr(self.llm, "vllm") is False
        ):
            if isinstance(text, Generator):
                assert (self.__class__.__name__ != "CosyVoiceModel") and not hasattr(
                    self.llm, "vllm"
                ), "streaming input text is only implemented for CosyVoice2/3 and do not support vllm!"
                token_generator = self.llm.inference_bistream(
                    text=text,
                    prompt_text=prompt_text.to(self.device),
                    prompt_text_len=torch.tensor(
                        [prompt_text.shape[1]], dtype=torch.int32
                    ).to(self.device),
                    prompt_speech_token=llm_prompt_speech_token.to(self.device),
                    prompt_speech_token_len=torch.tensor(
                        [llm_prompt_speech_token.shape[1]], dtype=torch.int32
                    ).to(self.device),
                    embedding=llm_embedding.to(self.device),
                )
            else:
                token_generator = self.llm.inference(
                    text=text.to(self.device),
                    text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(
                        self.device
                    ),
                    prompt_text=prompt_text.to(self.device),
                    prompt_text_len=torch.tensor(
                        [prompt_text.shape[1]], dtype=torch.int32
                    ).to(self.device),
                    prompt_speech_token=llm_prompt_speech_token.to(self.device),
                    prompt_speech_token_len=torch.tensor(
                        [llm_prompt_speech_token.shape[1]], dtype=torch.int32
                    ).to(self.device),
                    embedding=llm_embedding.to(self.device),
                    uuid=uuid,
                )
            for i in token_generator:
                if i in self.silent_tokens:
                    cur_silent_token_num += 1
                    if cur_silent_token_num > max_silent_token_num:
                        continue
                else:
                    cur_silent_token_num = 0
                self.tts_speech_token_dict[uuid].append(i)
        self.llm_end_dict[uuid] = True

    def vc_job(self, source_speech_token, uuid):
        self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist()
        self.llm_end_dict[uuid] = True

    def token2wav(
        self,
        token,
        prompt_token,
        prompt_feat,
        embedding,
        uuid,
        finalize=False,
        speed=1.0,
    ):
        with torch.cuda.amp.autocast(self.fp16):
            tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(
                token=token.to(self.device, dtype=torch.int32),
                token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(
                    self.device
                ),
                prompt_token=prompt_token.to(self.device),
                prompt_token_len=torch.tensor(
                    [prompt_token.shape[1]], dtype=torch.int32
                ).to(self.device),
                prompt_feat=prompt_feat.to(self.device),
                prompt_feat_len=torch.tensor(
                    [prompt_feat.shape[1]], dtype=torch.int32
                ).to(self.device),
                embedding=embedding.to(self.device),
                flow_cache=self.flow_cache_dict[uuid],
            )

        # mel overlap fade in out
        if self.mel_overlap_dict[uuid].shape[2] != 0:
            tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
        # append hift cache
        if self.hift_cache_dict[uuid] is not None:
            hift_cache_mel, hift_cache_source = (
                self.hift_cache_dict[uuid]["mel"],
                self.hift_cache_dict[uuid]["source"],
            )
            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
        else:
            hift_cache_source = torch.zeros(1, 1, 0)
        # keep overlap mel and hift cache
        if finalize is False:
            self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len :]
            tts_mel = tts_mel[:, :, : -self.mel_overlap_len]
            tts_speech, tts_source = self.hift.inference(
                speech_feat=tts_mel, cache_source=hift_cache_source
            )
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(
                    tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window
                )
            self.hift_cache_dict[uuid] = {
                "mel": tts_mel[:, :, -self.mel_cache_len :],
                "source": tts_source[:, :, -self.source_cache_len :],
                "speech": tts_speech[:, -self.source_cache_len :],
            }
            tts_speech = tts_speech[:, : -self.source_cache_len]
        else:
            if speed != 1.0:
                assert (
                    self.hift_cache_dict[uuid] is None
                ), "speed change only support non-stream inference mode"
                tts_mel = F.interpolate(
                    tts_mel, size=int(tts_mel.shape[2] / speed), mode="linear"
                )
            tts_speech, tts_source = self.hift.inference(
                speech_feat=tts_mel, cache_source=hift_cache_source
            )
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(
                    tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window
                )
        return tts_speech

    def tts(
        self,
        text=torch.zeros(1, 0, dtype=torch.int32),
        flow_embedding=torch.zeros(0, 192),
        llm_embedding=torch.zeros(0, 192),
        prompt_text=torch.zeros(1, 0, dtype=torch.int32),
        llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        prompt_speech_feat=torch.zeros(1, 0, 80),
        source_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        stream=False,
        speed=1.0,
        **kwargs
    ):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = (
                [],
                False,
            )
            self.hift_cache_dict[this_uuid] = None
            self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
            self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
        if source_speech_token.shape[1] == 0:
            p = threading.Thread(
                target=self.llm_job,
                args=(
                    text,
                    prompt_text,
                    llm_prompt_speech_token,
                    llm_embedding,
                    this_uuid,
                ),
            )
        else:
            p = threading.Thread(
                target=self.vc_job, args=(source_speech_token, this_uuid)
            )
        p.start()
        if stream is True:
            token_hop_len = self.token_min_hop_len
            while True:
                time.sleep(0.1)
                if (
                    len(self.tts_speech_token_dict[this_uuid])
                    >= token_hop_len + self.token_overlap_len
                ):
                    this_tts_speech_token = torch.tensor(
                        self.tts_speech_token_dict[this_uuid][
                            : token_hop_len + self.token_overlap_len
                        ]
                    ).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(
                        token=this_tts_speech_token,
                        prompt_token=flow_prompt_speech_token,
                        prompt_feat=prompt_speech_feat,
                        embedding=flow_embedding,
                        uuid=this_uuid,
                        finalize=False,
                    )
                    yield {"tts_speech": this_tts_speech.cpu()}
                    with self.lock:
                        self.tts_speech_token_dict[this_uuid] = (
                            self.tts_speech_token_dict[this_uuid][token_hop_len:]
                        )
                    # increase token_hop_len for better speech quality
                    token_hop_len = min(
                        self.token_max_hop_len,
                        int(token_hop_len * self.stream_scale_factor),
                    )
                if (
                    self.llm_end_dict[this_uuid] is True
                    and len(self.tts_speech_token_dict[this_uuid])
                    < token_hop_len + self.token_overlap_len
                ):
                    break
            p.join()
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(
                self.tts_speech_token_dict[this_uuid]
            ).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                finalize=True,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        else:
            # deal with all tokens
            p.join()
            this_tts_speech_token = torch.tensor(
                self.tts_speech_token_dict[this_uuid]
            ).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                uuid=this_uuid,
                finalize=True,
                speed=speed,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            self.mel_overlap_dict.pop(this_uuid)
            self.hift_cache_dict.pop(this_uuid)
            self.flow_cache_dict.pop(this_uuid)
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.current_stream().synchronize()


class CosyVoice2Model(CosyVoiceModel):

    def __init__(
        self,
        llm: torch.nn.Module,
        flow: torch.nn.Module,
        hift: torch.nn.Module,
        fp16: bool = False,
    ):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.llm = llm
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        # NOTE must matching training static_chunk_size
        self.token_hop_len = 25
        # hift cache
        self.mel_cache_len = 8
        self.source_cache_len = int(self.mel_cache_len * 480)
        # speech fade in out
        self.speech_window = np.hamming(2 * self.source_cache_len)
        # rtf and decoding related
        self.llm_context = (
            torch.cuda.stream(torch.cuda.Stream(self.device))
            if torch.cuda.is_available()
            else nullcontext()
        )
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.hift_cache_dict = {}
        self.silent_tokens = []

    def load_jit(self, flow_encoder_model):
        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
        self.flow.encoder = flow_encoder

    def load_vllm(self, model_dir):
        export_cosyvoice2_vllm(self.llm, model_dir, self.device)
        from vllm import EngineArgs, LLMEngine

        engine_args = EngineArgs(
            model=model_dir,
            skip_tokenizer_init=True,
            enable_prompt_embeds=True,
            gpu_memory_utilization=0.2,
        )
        self.llm.vllm = LLMEngine.from_engine_args(engine_args)
        self.llm.lock = threading.Lock()
        del self.llm.llm.model.model.layers

    def token2wav(
        self,
        token,
        prompt_token,
        prompt_feat,
        embedding,
        token_offset,
        uuid,
        stream=False,
        finalize=False,
        speed=1.0,
    ):
        with torch.cuda.amp.autocast(self.fp16):
            tts_mel, _ = self.flow.inference(
                token=token.to(self.device, dtype=torch.int32),
                token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(
                    self.device
                ),
                prompt_token=prompt_token.to(self.device),
                prompt_token_len=torch.tensor(
                    [prompt_token.shape[1]], dtype=torch.int32
                ).to(self.device),
                prompt_feat=prompt_feat.to(self.device),
                prompt_feat_len=torch.tensor(
                    [prompt_feat.shape[1]], dtype=torch.int32
                ).to(self.device),
                embedding=embedding.to(self.device),
                streaming=stream,
                finalize=finalize,
            )
        tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio :]
        # append hift cache
        if self.hift_cache_dict[uuid] is not None:
            hift_cache_mel, hift_cache_source = (
                self.hift_cache_dict[uuid]["mel"],
                self.hift_cache_dict[uuid]["source"],
            )
            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
        else:
            hift_cache_source = torch.zeros(1, 1, 0)
        # keep overlap mel and hift cache
        if finalize is False:
            tts_speech, tts_source = self.hift.inference(
                speech_feat=tts_mel, cache_source=hift_cache_source
            )
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(
                    tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window
                )
            self.hift_cache_dict[uuid] = {
                "mel": tts_mel[:, :, -self.mel_cache_len :],
                "source": tts_source[:, :, -self.source_cache_len :],
                "speech": tts_speech[:, -self.source_cache_len :],
            }
            tts_speech = tts_speech[:, : -self.source_cache_len]
        else:
            if speed != 1.0:
                assert (
                    self.hift_cache_dict[uuid] is None
                ), "speed change only support non-stream inference mode"
                tts_mel = F.interpolate(
                    tts_mel, size=int(tts_mel.shape[2] / speed), mode="linear"
                )
            tts_speech, tts_source = self.hift.inference(
                speech_feat=tts_mel, cache_source=hift_cache_source
            )
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(
                    tts_speech, self.hift_cache_dict[uuid]["speech"], self.speech_window
                )
        return tts_speech

    def tts(
        self,
        text=torch.zeros(1, 0, dtype=torch.int32),
        flow_embedding=torch.zeros(0, 192),
        llm_embedding=torch.zeros(0, 192),
        prompt_text=torch.zeros(1, 0, dtype=torch.int32),
        llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        prompt_speech_feat=torch.zeros(1, 0, 80),
        source_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        stream=False,
        speed=1.0,
        **kwargs
    ):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = (
                [],
                False,
            )
            self.hift_cache_dict[this_uuid] = None
        if source_speech_token.shape[1] == 0:
            p = threading.Thread(
                target=self.llm_job,
                args=(
                    text,
                    prompt_text,
                    llm_prompt_speech_token,
                    llm_embedding,
                    this_uuid,
                ),
            )
        else:
            p = threading.Thread(
                target=self.vc_job, args=(source_speech_token, this_uuid)
            )
        p.start()
        if stream is True:
            token_offset = 0
            prompt_token_pad = int(
                np.ceil(flow_prompt_speech_token.shape[1] / self.token_hop_len)
                * self.token_hop_len
                - flow_prompt_speech_token.shape[1]
            )
            while True:
                time.sleep(0.1)
                this_token_hop_len = (
                    self.token_hop_len + prompt_token_pad
                    if token_offset == 0
                    else self.token_hop_len
                )
                if (
                    len(self.tts_speech_token_dict[this_uuid]) - token_offset
                    >= this_token_hop_len + self.flow.pre_lookahead_len
                ):
                    this_tts_speech_token = torch.tensor(
                        self.tts_speech_token_dict[this_uuid][
                            : token_offset
                            + this_token_hop_len
                            + self.flow.pre_lookahead_len
                        ]
                    ).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(
                        token=this_tts_speech_token,
                        prompt_token=flow_prompt_speech_token,
                        prompt_feat=prompt_speech_feat,
                        embedding=flow_embedding,
                        token_offset=token_offset,
                        uuid=this_uuid,
                        stream=stream,
                        finalize=False,
                    )
                    token_offset += this_token_hop_len
                    yield {"tts_speech": this_tts_speech.cpu()}
                if (
                    self.llm_end_dict[this_uuid] is True
                    and len(self.tts_speech_token_dict[this_uuid]) - token_offset
                    < this_token_hop_len + self.flow.pre_lookahead_len
                ):
                    break
            p.join()
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(
                self.tts_speech_token_dict[this_uuid]
            ).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                token_offset=token_offset,
                uuid=this_uuid,
                finalize=True,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        else:
            # deal with all tokens
            p.join()
            this_tts_speech_token = torch.tensor(
                self.tts_speech_token_dict[this_uuid]
            ).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(
                token=this_tts_speech_token,
                prompt_token=flow_prompt_speech_token,
                prompt_feat=prompt_speech_feat,
                embedding=flow_embedding,
                token_offset=0,
                uuid=this_uuid,
                finalize=True,
                speed=speed,
            )
            yield {"tts_speech": this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            self.hift_cache_dict.pop(this_uuid)
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.current_stream().synchronize()

    def reconstruct(
        self,
        text=torch.zeros(1, 0, dtype=torch.int32),
        flow_embedding=torch.zeros(0, 192),
        llm_embedding=torch.zeros(0, 192),
        prompt_text=torch.zeros(1, 0, dtype=torch.int32),
        llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        prompt_speech_feat=torch.zeros(1, 0, 80),
        source_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        stream=False,
        speed=1.0,
        **kwargs
    ):
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = (
                [],
                False,
            )
            self.hift_cache_dict[this_uuid] = None
        this_tts_speech = self.token2wav(
            token=llm_prompt_speech_token,
            prompt_token=flow_prompt_speech_token,
            prompt_feat=prompt_speech_feat,
            embedding=flow_embedding,
            token_offset=0,
            uuid=this_uuid,
            finalize=True,
            speed=speed,
        )
        yield {"tts_speech": this_tts_speech.cpu()}


class CosyVoice3Model(CosyVoice2Model):

    def __init__(
        self,
        llm: torch.nn.Module,
        flow: torch.nn.Module,
        hift: torch.nn.Module,
        fp16: bool = False,
    ):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.llm = llm
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        # NOTE must matching training static_chunk_size
        self.token_hop_len = 25
        # rtf and decoding related
        self.llm_context = (
            torch.cuda.stream(torch.cuda.Stream(self.device))
            if torch.cuda.is_available()
            else nullcontext()
        )
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.hift_cache_dict = {}
        # FSQ silent and breath token
        self.silent_tokens = [1, 2, 28, 29, 55, 248, 494, 2241, 2242, 2322, 2323]

    def token2wav(
        self,
        token,
        prompt_token,
        prompt_feat,
        embedding,
        token_offset,
        uuid,
        stream=False,
        finalize=False,
        speed=1.0,
    ):
        with torch.cuda.amp.autocast(self.fp16):
            tts_mel, _ = self.flow.inference(
                token=token.to(self.device, dtype=torch.int32),
                token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(
                    self.device
                ),
                prompt_token=prompt_token.to(self.device),
                prompt_token_len=torch.tensor(
                    [prompt_token.shape[1]], dtype=torch.int32
                ).to(self.device),
                prompt_feat=prompt_feat.to(self.device),
                prompt_feat_len=torch.tensor(
                    [prompt_feat.shape[1]], dtype=torch.int32
                ).to(self.device),
                embedding=embedding.to(self.device),
                streaming=stream,
                finalize=finalize,
            )
            tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio :]
            # append mel cache
            if self.hift_cache_dict[uuid] is not None:
                hift_cache_mel = self.hift_cache_dict[uuid]["mel"]
                tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
                self.hift_cache_dict[uuid]["mel"] = tts_mel
            else:
                self.hift_cache_dict[uuid] = {"mel": tts_mel, "speech_offset": 0}
            if speed != 1.0:
                assert (
                    token_offset == 0 and finalize is True
                ), "speed change only support non-stream inference mode"
                tts_mel = F.interpolate(
                    tts_mel, size=int(tts_mel.shape[2] / speed), mode="linear"
                )
            tts_speech, _ = self.hift.inference(speech_feat=tts_mel, finalize=finalize)
            tts_speech = tts_speech[:, self.hift_cache_dict[uuid]["speech_offset"] :]
            self.hift_cache_dict[uuid]["speech_offset"] += tts_speech.shape[1]
        return tts_speech